lawn mower" - the drone combs the target area, moving in strips,
and an algorithm similar to Evers' algorithm, but less advanced in terms of working with probability distribution maps.
In virtual testing, Evers' algorithm beat both approaches on two key metrics: the distance the drone would have to fly before finding a missing person and the probability that the person would be found.
The terrain sweep and the existing algorithmic approach helped find a person in 8% and 12% of cases, respectively. Evers's approach showed an efficiency of 19%. If the system proves successful in real-world malta number data conditions, it could speed up response times and save more lives in situations where every minute counts.
efficient routes and faster retrieval of missing persons in the wild, depending on how suitable the environment is for drone searches (for example, it is more difficult to explore a dense forest than a thicket).
But there are nuances to consider. The success of such a planning algorithm will depend on the accuracy of the probability maps. And if you rely too much on them, there is a risk that drone operators will spend too much time studying the wrong areas.
Evers says the next important step is to get as much data as possible to train on. To do this, he hopes to use GPS data from later rescues to help the model understand the connection between where a person was last seen and where they were eventually found.
However, transaction records are not always detailed enough to be workable.